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Kernel sparse representation-based classifier ensemble for face recognition

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Abstract

Kernel sparse representation-based classifier (KSRC) has been proposed, which has good representation and classification performance on face image data. The performance of KSRC on face image data is partly dependent on the random projection matrix when using the random projection method and the kernel Gram matrix. This paper develops the kernel sparse representation-based classifier ensemble (KSRCE), which does not require to consider the effect of random projection and kernel Gram matrix on KSRC. Actually, the random projection matrix and the kernel Gram matrix could be used for designing the diversity schemes for KSRCE. In the combination stage, we can combine the labels or the reconstruction errors of a test sample. Experimental results on three face data sets show that KSRCE is very promising.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Li Zhang & Fan-Zhang Li

  2. AI Speech Ltd., Suzhou, 215123, China

    Wei-Da Zhou

Authors
  1. Li Zhang
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  2. Wei-Da Zhou
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  3. Fan-Zhang Li
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Correspondence to Li Zhang.

Additional information

Supported in part by the National Natural Science Foundation of China under Grant Nos. 60970067 and 61033013, by the Natural Science Foundation of Jiangsu Province of China under Grant Nos. BK2011284, BK201222725, by the Natural Science Pre-research Project of Soochow University under Grant No. SDY2011B09 and by the Qing Lan Project.

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Zhang, L., Zhou, WD. & Li, FZ. Kernel sparse representation-based classifier ensemble for face recognition. Multimed Tools Appl 74, 123–137 (2015). https://doi.org/10.1007/s11042-013-1457-1

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